Twenty units, in order, each one a single trade-off.
Decision prompts, per-criterion calibrated grading, spaced review.
Not a tutorial app.
Sideload the signed APK directly. Google Play Protect verifies on install. The in-app banner surfaces when a new version is available.
- Download the latest APK · Android 8.0 (API 26)+
- SHA-256 fingerprint for verification
- All releases & notes: GitHub Releases
Perpenda helps product professionals become AI-fluent enough to lead the decisions their teams now have to make.
The intended audience is product-side professionals with real stakes in AI literacy:
- Product managers
- Product marketing managers
- Founders
- Design leads
- Business-development leads
- Executives and product-adjacent decision-makers
The goal is decision-grade competence: enough fluency to make build / buy / skip decisions, talk credibly with engineers, and recognize trade-offs and failure modes in AI-backed products.
Perpenda is not a math-first ML course, a code-first engineering curriculum, a glossary-first reference app, or a hype-feed about AI news.
v1.1. Builds on the v1.0 sideload release (first shipped 1 June 2026) with account hardening and a stricter learning loop. The signed APK is hosted on GitHub Releases; the app surfaces an in-app banner when a new version is available.
What v1.1 ships:
- Fifteen units across the LLM-systems curriculum (tokenization through safety + content moderation), in a strict prerequisite order enforced both in-app and server-side.
- Per-criterion calibrated grading on every decision prompt — no holistic score. A unit completes only when at most one criterion is missed and the answer isn't flagged.
- Required email verification at signup and in-app password reset, both via one-time codes.
- Spaced review on completed units.
- Cross-device progress sync once signed in.
Held back, planned for follow-up releases:
- Units 16–20 — the operating-phase units (monitoring, vendor risk, A/B, fallbacks). Placeholders exist in the path; authored content lands as real-use signal decides which production topics matter most.
- F6 Path Overview.
- Google Play production distribution. The app is in Play closed testing; sideload via GitHub Releases remains the public channel until production access clears.
Canonical status docs:
docs/strategy/STRATEGY.md— product strategy and locked decisions.docs/roadmap/EXECUTION.md— phase plan and sequencing.docs/roadmap/PHASE_3_4_ROADMAP.md— Phase 3 / Phase 4 roadmap (historical at this point).docs/curriculum/v1-path-outline.md— the canonical 20-unit path outline.
The app is organized around a path, not a catalog.
The intended session loop is:
- Continue — open the app and see the next unit in the path.
- Bite — read a short, trade-off-first explanation.
- Decide — answer an open-ended decision prompt.
- Calibrate — see how the answer maps to a rubric, sources, and confidence.
- Progress — complete the unit and advance through the path.
- Return — revisit older units through spaced review.
The glossary exists as supporting reference material, not as the primary product surface.
The canonical v1 path is LLM Systems for PMs.
The path is designed to teach product professionals how to reason about LLM-backed products through concrete product trade-offs.
Published units (1–15):
- Tokenization
- Context Window
- Latency
- Evals
- Model selection
- Prompt design basics
- Hallucination + reliability
- Cost dynamics at scale
- Fine-tuning vs. prompting vs. RAG
- Vector search / RAG fundamentals
- Streaming UX
- Tool use / function calling
- Multimodal (vision basics)
- Agents / multi-step reasoning
- Safety + content moderation
Locked / planned units:
16–20. Operating-phase units, to be locked from real-user signal
See docs/curriculum/v1-path-outline.md for the maintained source of truth.
Perpenda is guided by five product principles:
- Decisions before mechanism — teach what to do with a concept before diving into how it works.
- Calibrate, don't bluff — claims should be sourced, confidence-tagged, and honest about uncertainty.
- Path, not catalog — the home experience is continuing the learning path, not browsing a glossary.
- Bite first, depth on tap — every unit should be understandable quickly, with depth available when needed.
- Quality ceiling, not content scale — better to ship fewer excellent units than many mediocre ones.
The primary wedge is the combination of trade-off-first pedagogy and calibrated reliability.
The repo contains an Android client, a FastAPI backend, PostgreSQL migrations, authored curriculum content, regression sets, and project documentation.
- Kotlin
- Jetpack Compose
- Material 3
- JWT-backed auth state
- Encrypted token storage
- Path home, unit reader, auth, settings, and supporting glossary surfaces
- FastAPI
- PostgreSQL
- psycopg
- JWT auth
- Migration runner
- Path / unit / completion APIs
- LLM grading service
- Regression-set discipline for grader calibration
- Railway-oriented backend deployment
- Production config validation through
APP_ENV=production - PostgreSQL migration discipline
- Prompt-caching strategy for grader unit economics
Backend decisions are documented in docs/guides/BACKEND_BEST_PRACTICES.md.
Android decisions are documented in docs/guides/ANDROID_BEST_PRACTICES.md.
app/ Android client
backend/ FastAPI backend, migrations, scripts, tests
content/units/ Authored learning units
content/regression-sets/ Ground-truth answer/grade regression sets
docs/ Canonical strategy, execution, audit, and roadmap docs
gradle/ Android Gradle wrapper files
scripts/ Project utility scripts
Important docs:
docs/strategy/STRATEGY.md Product strategy
docs/roadmap/EXECUTION.md Phase plan
docs/audits/AUDIT.md Phase 0 cleanup audit and keep/reshape/delete map
docs/roadmap/PHASE_3_4_ROADMAP.md Phase 3/4 roadmap
docs/guides/ANDROID_BEST_PRACTICES.md Android implementation decisions
docs/guides/BACKEND_BEST_PRACTICES.md Backend implementation decisions
docs/curriculum/v1-path-outline.md Canonical v1 unit sequence
This section is intentionally minimal until the setup flow is stabilized.
Typical backend workflow:
python3 -m venv .venv
source .venv/bin/activate
pip install -r backend/requirements.txt
python3 -m backend.scripts.migrate
python3 -m backend.scripts.seed_db
uvicorn backend.app.main:app --host 0.0.0.0 --port 8000Environment variables are expected for production-like runs, especially:
DATABASE_URL
JWT_SECRET
AI_PROVIDER_API_KEY
APP_ENV
Production deployments should set APP_ENV=production so weak defaults are rejected at startup.
Typical Android workflow:
./gradlew assembleDebugThe app is developed in Android Studio and currently targets Android-first v1 development.
The product's credibility depends on the grader being trustworthy.
Each published unit is expected to ship with a ground-truth regression set. The grader is evaluated against authored expected outcomes before a unit is considered published.
The grading model is per-criterion, not holistic. The intended user-facing behavior is:
- show which criteria were met or not met;
- expose confidence;
- flag uncertain answers instead of pretending certainty;
- ground grading in the unit content, sources, rubric, and quoted user answer text.
This is part of the product's reliability moat, not an optional test harness.
Some old glossary-oriented features were intentionally demoted or removed as the product moved from its FOSS-101 / AI-101 origins to Perpenda.
Examples of legacy or demoted surfaces:
- Browse / Categories / Search as primary navigation
- Ask Glossary as a front door
- Term Draft contribution flows
- AI Learning Layer style-picker flows
- Glossary-first home screen patterns
The current source of truth for what survives, changes, or gets removed is docs/audits/AUDIT.md.
This project is licensed under the GNU General Public License v3.0.
See LICENSE for the full license text.